AI BriefingThe MoE Structure and How It Works in Transformers
Key point
This explains the principles and advantages of the MoE (Mixture of Experts) structure, which maximizes the computational efficiency of Transformers.
Details
Dense Scaling for improving LLM performance is facing limits in cost and latency. To address this, MoE (Mixture of Experts) technology is emerging as a key solution, replacing the Transformer's feedforward layers with multiple Expert networks, where a Router selects the appropriate expert for each token.
The core idea of MoE is to maximize efficiency by reducing the number of parameters activated during inference while maintaining the model's overall capacity. For example, even a model with 21B total parameters can use only about 3.6B parameters during inference, achieving both the performance of a large-scale model and the speed of a small model at the same time.
Key Advantages:
- Computational Efficiency: Delivers better performance than Dense models within the same computational budget (FLOPs).
- Ease of Parallelization: Computation can be distributed on a per-expert basis, enabling Expert Parallelism.
- Industry Adoption: Major recent open models such as DeepSeek, Qwen, and Mixtral are actively adopting the MoE structure.
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